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Home
In-sensor-memory Computing For Post-von Neumann Intelligence: A Perspective.

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In-sensor-memory Computing For Post-von Neumann Intelligence: A Perspective.

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In-Sensor-Memory Computing for Post-Von Neumann Intelligence: A Perspective.

Hongyu Tang^1,2, Ninghai Yu³, Pengsheng Min³

¹College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, People's Republic of China. hongyu_tang@fudan.edu.cn.

Nano-Micro Letters

|April 19, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

In-sensor-memory computing (ISMC) overcomes limitations of traditional architectures by integrating processing and memory. This approach enables energy-efficient, distributed artificial intelligence at the data source.

Keywords:

In-sensor-memory computing (ISMC)Industry–academia–research (IAR)Neuromorphic hardware Post-von Neumann intelligence

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Area of Science:

Computer Science
Materials Science
Electrical Engineering

Background:

Conventional von Neumann architectures face limitations due to data movement between sensing, memory, and computation.
The slowing of transistor scaling necessitates architectural innovation for advancing intelligent systems.

Purpose of the Study:

To survey technological foundations, architectural trends, and applications of In-Sensor-Memory Computing (ISMC).
To examine global collaborations and identify challenges in ISMC development.

Main Methods:

Co-locating perception, storage, and computation within unified device and system architectures.
Leveraging advances in memristive/ferroelectric devices, novel materials, 3D integration, and neuromorphic architectures.
Utilizing co-evolved algorithms like spiking neural networks and reservoir computing.

Main Results:

In-sensor-memory computing (ISMC) enables in situ signal processing, mixed-signal computation, and event-driven intelligence.
Recent technological and algorithmic advancements have expanded the capabilities of ISMC platforms.
ISMC offers a pathway to overcome the bottlenecks of traditional computing architectures.

Conclusions:

ISMC is a promising post-von Neumann hardware paradigm for energy-efficient, distributed intelligence.
Addressing challenges in variability, reliability, scalability, and benchmarking is crucial for ISMC's advancement.
ISMC is poised to revolutionize intelligent systems by enabling computation at the data source.